This invention relates generally to intelligent electronic devices (IEDs), e. g., electronic trip units, protective relays, circuit breakers, energy meters, power quality meters, power monitors, power analyzers, motor control center controllers and relays, programmable logic controllers, programmable logic controller I/Os, and loop-controllers. More specifically, the present invention relates to a method of applying statistical analysis techniques in intelligent electronic devices for data reduction and analysis.
Intelligent electronic devices, e. g., electronic trip units, protective relays, circuit breakers, energy meters, power quality meters, power monitors, power analyzers, motor control center controllers and relays, programmable logic controllers, programmable logic controller I/Os, and loop-controllers are well known. These devices store real time information within hundreds of registers on measured and/or calculated parameters such as energy, temperature, power quality, electrical parameters, and device status. This information is often periodically communicated, by way of a network, to a centralized monitoring station or processor. However, this information is difficult to analyze due to the high volume of data. Also, if the sampling frequency for sending this data is not sufficiently high, i.e. finely granular, then there is a possibility of missing peak conditions.
It is therefore seen to be desirable to apply statistical analysis techniques in an intelligent electronic device for data reduction and analysis. A method of statistical analysis in an intelligent electronic device utilizing a statistical analysis algorithm in the microcontrol of the intelligent electronic device is presented. An electronic trip unit is described herein by way of exemplary embodiment only, as the present invention applies to other intelligent electronic devices as well. The electronic trip unit comprising voltage and current sensors which provide analog signals indicative of the power line signals. The analog signals are converted by an A/D (analog/digital) converter to digital signals which are processed by a microcontrol. The trip unit further includes RAM (random access memory), ROM (read only memory) and EEPROM (electronic erasable programmable read only memory) all of which communicate with the microcontrol. The ROM includes trip unit application code, e.g., main functionality firmware, including initializing parameters, and boot code. The application code includes code for the statistical analysis algorithm of the present invention. The EEPROM includes operational parameters, e.g., code for setting a user selectable number of standard deviations from a mean, for the application code. These parameters may be stored in the trip unit at the factory and are selected to meet customersxe2x96xa1 requirements, but can also be remotely downloaded.
In an exemplary embodiment of the invention real time data of electrical parameters measured and/or calculated by the intelligent electronic device is processed by a statistical analysis algorithm using recognized statistical and/or numerical techniques. The algorithm issues events and/or alarms when observed data is (value) recognized to deviate statistically significantly from an expected range, e.g., an observed value is N (preferably N=3) standard deviations greater or smaller than an expected mean value. In a preferred embodiment, the expected range is a designated number of standard deviations (preferably 3, but alternatively may be designated by a user) from the mean value. The statistical analysis algorithm is performed in the intelligent electronic device itself. Therefore the amount of data required to be transmitted from the intelligent electronic device to a central monitoring station is drastically reduced since only statistically significant deviations need to be communicated. It is significantly less difficult for a user (at a receiving computer) to analyze this information due to the lower volume of data. The means and standard deviations are calculated by the intelligent electronic device itself by analyzing parameters measured over time or user input.
Furthermore, the statistical techniques algorithm makes possible more reliable and accurate detection of developing fault conditions. If the volume of data is not properly analyzed, then preventable fault conditions may be allowed to build up over time. Eventually these fault conditions may become sufficiently severe to cause a major fault event, e.g., a circuit breaker tripping. Once this happens, damage and costly delays to facility operations will have occurred. By reducing the amount of data to be reviewed manually by a user, by several orders of magnitude, the algorithm enhances early detection of these developing fault conditions. In a large facility with hundreds of intelligent electronic devices, this represents a major benefit.